Knowledge-guided robust MRI brain extraction for diverse large-scale neuroimaging studies on humans and non-human primates.
Knowledge-guided robust MRI brain extraction for diverse large-scale neuroimaging studies on humans and non-human primates.
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知识引导的稳健 MRI 大脑提取，用于人类和非人类灵长类动物的各种大规模神经影像研究
DOI：
10.1371/journal.pone.0077810
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
中科院分区：
文献类型：
--
作者：
Wang Y;Nie J;Yap PT;Li G;Shi F;Geng X;Guo L;Shen D;Alzheimer's Disease Neuroimaging Initiative
准确和鲁棒的大脑提取是大多数神经成像分析流程中的关键步骤。特别地，对于涉及具有不同年龄和诊断组的大量受试者的大规模多部位神经成像研究，自动且一致地准确且鲁棒地提取大脑是非常期望的。在本文中，我们引入特定人群的概率图来指导不同受试者群体的大脑提取，包括健康和患病的成年人群体，发育中和老龄化的人群，以及非人灵长类动物。具体而言，所提出的方法结合了一个基于图集的方法，粗糙的头骨剥离，与变形表面为基础的方法，是由局部强度信息和人口特定的先验信息从一组真实的大脑图像更本地化的细化。对ADNI数据集的800多名受试者的不同大规模人群进行了全面的定量评价（55 - 90岁，多中心，各种诊断组），OASIS数据集，超过400例受试者（18 - 96岁，年龄范围广，各种诊断组）和NIH儿科数据集（150例受试者）（5 - 18岁，多中心，广泛的年龄范围，作为成人数据集的补充年龄组）。结果表明，我们的方法在几乎整个人类寿命中始终产生最佳的整体结果，只有一组参数。为了证明其在非人类灵长类动物上工作的能力，使用具有20个受试者的恒河猴数据集进一步评估所提出的方法。与BET、Two-pass BET、BET-B、BSE、HWA、ROBEX和AFNI等常用方法的定量比较表明，该方法在所有测试数据集上均表现出良好的上级性能，表明了其鲁棒性和有效性。
Accurate and robust brain extraction is a critical step in most neuroimaging analysis pipelines. In particular, for the large-scale multi-site neuroimaging studies involving a significant number of subjects with diverse age and diagnostic groups, accurate and robust extraction of the brain automatically and consistently is highly desirable. In this paper, we introduce population-specific probability maps to guide the brain extraction of diverse subject groups, including both healthy and diseased adult human populations, both developing and aging human populations, as well as non-human primates. Specifically, the proposed method combines an atlas-based approach, for coarse skull-stripping, with a deformable-surface-based approach that is guided by local intensity information and population-specific prior information learned from a set of real brain images for more localized refinement. Comprehensive quantitative evaluations were performed on the diverse large-scale populations of ADNI dataset with over 800 subjects (55∼90 years of age, multi-site, various diagnosis groups), OASIS dataset with over 400 subjects (18∼96 years of age, wide age range, various diagnosis groups), and NIH pediatrics dataset with 150 subjects (5∼18 years of age, multi-site, wide age range as a complementary age group to the adult dataset). The results demonstrate that our method consistently yields the best overall results across almost the entire human life span, with only a single set of parameters. To demonstrate its capability to work on non-human primates, the proposed method is further evaluated using a rhesus macaque dataset with 20 subjects. Quantitative comparisons with popularly used state-of-the-art methods, including BET, Two-pass BET, BET-B, BSE, HWA, ROBEX and AFNI, demonstrate that the proposed method performs favorably with superior performance on all testing datasets, indicating its robustness and effectiveness.
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